Applicability domain: A step toward confident predictions and decidability for QSAR modeling

Supratik Kar, Kunal Roy, Jerzy Leszczynski

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

86 Scopus citations

Abstract

In the context of human safety assessment through quantitative structure–activity relationship (QSAR) modeling, the concept of applicability domain (AD) has an enormous role to play. The Organization of Economic Co-operation and Development (OECD) for QSAR model validation recommended as principle 3 “A defined domain of applicability” to be present for a predictive QSAR model. The study of AD allows estimating the uncertainty in the prediction for a particular molecule based on how similar it is to the training compounds which are used in the model development. In the current scenario, AD represents an active research topic, and many methods have been designed to estimate the competence of a model and the confidence in its outcome for a given prediction task. Thus, characterization of interpolation space is significant in defining the AD. The diverse set of reported AD methods was constructed through different hypotheses and algorithms. These multiplicities of methodologies mystify the end users and make the comparison of the AD for different models a complex issue to address. We have attempted to summarize in this chapter the important concepts of AD including particulars of the available methods to compute the AD along with their thresholds and criteria for estimating AD through training set interpolation in the descriptor space. The idea about transparent domain and decision domain are also discussed. To help readers determine the AD in their projects, practical examples together with available open source software tools are provided.

Original languageEnglish
Title of host publicationMethods in Molecular Biology
PublisherHumana Press Inc.
Pages141-169
Number of pages29
DOIs
StatePublished - 2018

Publication series

NameMethods in Molecular Biology
Volume1800
ISSN (Print)1064-3745

Keywords

  • Applicability domain
  • Confidence
  • In silico
  • QSAR
  • Reliability

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